[2505.18600] Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment

Computer Science > Computer Vision and Pattern Recognition arXiv:2505.18600 (cs) [Submitted on 24 May 2025 (v1), last revised 10 Apr 2026 (this version, v3)] Title:Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment Authors:Bryan Sangwoo Kim, Jeongsol Kim, Jong Chul Ye View a PDF of the paper titled Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment, by Bryan Sangwoo Kim and 2 other authors View PDF HTML (experimental) Abstract:Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond that regime. We address this scalability bottleneck with Chain-of-Zoom (CoZ), a model-agnostic framework that factorizes SISR into an autoregressive chain of intermediate scale-states with multi-scale-aware prompts. CoZ repeatedly re-uses a backbone SR model, decomposing the conditional probability into tractable sub-problems to achieve extreme resolutions without additional training. Because visual cues diminish at high magnifications, we augment each zoom step with multi-scale-aware text prompts generated by a vision-language model (VLM). The prompt extractor itself is fine-tuned using Generalized Reward Policy Optimization (GRPO) with a critic VLM, aligning text guidance towards human preference. Experiments show that a standard 4x diffusion SR model wrapped in CoZ attains beyond 256x enlargement ...